SmartNEM: Smart Community Neighborhood - driven by energy informatics
About the project
SmartNEM is a joint project with University of Stavanger, Norway. The vision of the research is to exploit state-of-the-art ICT methods, tools and techniques for the future sustainable energy systems (this is what energy informatics is about). We have particular interest in the following technologies for efficient and secure energy systems: fog computing, machine/deep learning, data analytics, blockchain and software defined principles. We will develop new models and algorithms to provide the power grid operators with intelligent energy management with privacy preservation in local regions. The duration of the project is 2017 - 2021.
Investigate an ICT driven decentralized grid infrastructure supported by prosumers. It would allow DSOs and TSOs to achieve community level grid reliability, peak shaving and secure supply. A fog computing based information intensive data hub, blockchain and machine/deep learning techniques will be utilized for creating a self-organizing and self-optimizing, integrated, secure and privacy preserving community energy management system.
H.-M. Chung, S. Maharjan, Y. Zhang, F. Eliassen, T. Yuan, Edge Intelligence Empowered UAVs for Automated Wind Farm Monitoring in Smart Grids, IEEE Global Communication Conference: Selected Areas in Communication: Smart Grid Communications & Power Line Communication (GlobeCOM), 7 - 11 December 2020, Tapei, Taiwan (to appear)
A. M. A. Ahmed, Y. Zhang, F. Eliassen, Generative Adversarial Networks and Transfer Learning for Non-Intrusive Load Monitoring in Smart Grids, IEEE International Conf. on Communication, Control and Computing Technologies in Smart Grids (SmartGridComm), 11-13 November 2020 // Virtual Conference (to appear)
H.-M. Chung, S. Maharjan, Y. Zhang, F. Eliassen, Intelligent Charging Management of Electric Vehicles Considering Dynamic User Behaviour and Renewable Energy: A Stochastic Game Approach, IEEE Transaction on Intelligent Transportation Systems, 2020 ISSN 1524-9050, doi: 10.1109/TITS.2020.3008279, https://arxiv.org/abs/2006.16095
H.-M. Chung, S. Maharjan, Y. Zhang, F. Eliassen, Distributed Deep Reinforcement Learning for Intelligent Load Scheduling in Residential Smart Grid, IEEE Transaction on Industrial Informatics, 2020, ISSN 1551-3203, doi: 10.1109/TII.2020.3007167
H.-M. Chung, S. Maharjan, Y. Zhang, F. Eliassen, K. Strunz, Placement and Routing Optimization for Automated Inspection with UAVs: A Study in Offshore Wind Farm, IEEE Transactions on Industrial Informatics, 2020, ISSN 1551-3203, doi: 10.1109/TII.2020.3004816, https://arxiv.org/abs/2006.08326
S. Mohammadi, F. Eliassen, Y. Zhang, Effects of false data injection attacks on a local P2P energy trading market with prosumers, Proceedings of IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, The Netherlands, 25-28 Oct 2020 (to appear)
M. Zhang, F. Eliassen A. Taherkordi, H.-J. Jacobsen, H.-W. Chung, Energy Trading with Demand Response in a Community-based P2P Energy Market, Proceedings of IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Beijing, China, 21-23 Oct 2019
Popular science articles:
Når nabolaget forsyner seg selv med strøm (interview with M. Zhang and F. Eliassen), Europower-Energi Magazine, No. 5, 2020
The growing field of energy informatics (interview with Hwei-Ming Chung), UiO:Energy, Aug. 2020
The project is funded by the EnergiX programme of the Research Council of Norway (RCN). The total budget is 25 MNOK including contribution from Statnett (the Norwegian TSO) and Lyse Energy. The project will employ a total of seven PhD scholars of which three will be employed by University of Oslo (UiO).
University of Stavanger (coordinator), University of Oslo, Statnett (the Norwegian TSO), Lyse Energy, DNV-GL and the NCE Smart cluster of energy companies. The industry partners will offer industrial guidance to the PhD scholars.